This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.

The objective of this study is to examine the relationship between government expenditure (at aggregate as well as disaggregate level) and economic growth for Pakistan. The study further aims to find causal relationship for existence of applicability of Wagner’s or Keynesian hypothesis. Engle and Granger (1987) two step procedure and Granger causality test (1969) are employed for time series data for the period 1976 to 2015. Results suggest that only expenditure on social, economic and education services have proposed long-run association with economic growth in five of basic versions of Wagner’s law for Pakistan. The causality tests are showing mix results regarding existence of applicability of Wagner’s or Keynesian hypothesis. Expenditure on current subsidies, expenditure on defence, current expenditure and developmental expenditure are in favor of Wagner’s law in most of the cases, where causality runs from economic growth to government expenditure. Results of expenditure on social, economic and education services are in line with existence of Keynesian hypothesis, where causality flows from government expenditure to economic growth. On the basis of results, one may conclude that government should invest for expenditure on social, economic and education services to achieve sustainable economic growth by spending more on human resource development.

Major differences from previous work: For level 2 catch: Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations. Strata for which catches in tons are raised to match nominal data have had their numbers removed. Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets. For level 0 effort : In some datasets—namely those from ICCAT and the purse seine (PS) data from WCPFC— same effort data has been reported multiple times by using different units which have been kept as is, since no official mapping allows conversion between these units. As a result, users have be remind that some ICCAT and WCPFC effort data are deliberately duplicated : in the case of ICCAT data, lines with identical strata but different effort units are duplicates reporting the same fishing activity with different measurement units. It is indeed not possible to infer strict equivalence between units, as some contain information about others (e.g., Hours.FAD and Hours.FSC may inform Hours.STD). in the case of WCPFC data, effort records were also kept in all originally reported units. Here, duplicates do not necessarily share the same “fishing_mode”, as SETS for purse seiners are reported with an explicit association to fishing_mode, while DAYS are not. This distinction allows SETS records to be separated by fishing mode, whereas DAYS records remain aggregated. Some limited harmonization—particularly between units such as NET-days and Nets—has not been implemented in the current version of the dataset, but may be considered in future releases if a consistent relationship can be established.

Major differences from previous work: For level 2: Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations. Strata for which catches in tons are raised to match nominal data have had their numbers removed. Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The aim of this dataset was to be able to map each surface water works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use.

This has been clipped to the Gloucester PAE.

Dataset History

1. Joe Bell (GA) has clipped the surface water licences to the Gloucester PAE. This clip contains the works associated with the sharecomponent/ entitlement. This can be a many to many relationship. . A work is a surface water extraction point.

SW_licences_GloucesterPAE_Clip.dbf

1. Share component/ entitlement information was stored in the SW_Gloucester_COMBINED_v4.csv worksheet

2. Total volume of share component/ entitlement is 10,786ML

3. The works and share/component information was joined using Access, linking the CWlicence to the WAorCA_link. This links the volumetric entitlement to the works location.

4. This link also created share components that had a 0 entitlement which are licences that have been converted to unbundled licences in the new Water Act

5. By filtering out the 0 entitlement, the number of works linked to a share/component or entitlement with a specified volume was 212 with a total of 10,786ML. Worksheet FilteredIndividualSWLicences

6. Where there was more than one works per licence, an additional column was add COUNT_CWLICENSE. This shows where the share component/ entitlement is double counted as it is matched to each work with the full allocation.

7. An additional column was added SHARE_PER_WORKS which divides the share component/ entitlement by the number of works to give an allocation per works.

8. The SHARE_PER_WORKS column allows you to plot the works with the share component in ArcGIS without double counting the allocation.

9. A glossary of terms used ini the water licensing is included here: http://registers.water.nsw.gov.au/wma/Glossary.jsp

10. An additional worksheet was added to aggregate the data into Water Sources and Management Zones. The Water Sources and Management Zones were provided by NSW Office of Water

CombinedWSP_WSOURCES_31July2013.gdb\Geographic_GDA94\WSP_COMBINED_31July2013

1. The Avon River does not have management zones. Therefore data can only be viewed for the water source.

2. All other works can be aggregated to the Water Source, or the management zone depending on how you want to aggregate or disaggregate the data.

relevant fields:

CWLICENSE: works licence number

COUNT_CWLICENSE: Where there was more than one works per licence

SHARE_PER_WORKS: Share component divided by number of works to ensure no double counting

STATUS_DES: Status description as active, current, cancelled

LICENCE_iS: licensed issued date

LICENCE_LO: licence lodged date

LICENCE_P: Licence purpose eg. stock and domestic, town supply, irrigation

WORK_TYPE: pump, excavation etc

WORK_TYPE_: diversion or storage

MAJOR_CATC: major surface water catchement

NAME_OF_TH: water sharing plan the licence belongs to

WATER_SHAR: water sharing plan the licence belongs to

WATER_SOUR: water source

MANAGEMENT: management zone

WSP_STATUS: Status of the water sharing plan

START_DATE: Start date of the water sharing plan

END_DATE: end date of the water sharing plan

LICENSEorAPPROVAL: licence or approval number

Status: Cancelled or current (or blank)

ShareC: Share component attached to the licence

WAorCAlink:a combined water supply works / water use approval

LINKED_TO_AL:This is the identification number for an access licence which is shown on the licence certificate or on a search printout of the licence obtained from the access licence register run by Land and Property Information.

Dataset Citation

Bioregional Assessment Programme (XXXX) NSW Office of Water SW licences - Gloucester PAE v2 21022014. Bioregional Assessment Derived Dataset. Viewed 14 June 2016, http://data.bioregionalassessments.gov.au/dataset/f0a75a2b-233f-40a4-82cb-1929f2bee8c6.

Dataset Ancestors

• Derived From Subcatchment boundaries within and nearby the Gloucester subregion

• Derived From Bioregional Assessment areas v02

• Derived From Australian Coal Basins

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012.

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From BA ALL mean annual flow for NSW - Choudhury implementation of Budyko runoff v01

• Derived From Bioregional Assessment areas v01

• Derived From Geofabric Hydrology Reporting Catchments - V2.1

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From NSW Office of Water SurfaceWater licences in the Gloucester PAE

• Derived From GEODATA TOPO 250K Series 3

• Derived From Australian Geological Provinces, v02

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Gloucester Coal Basin

• Derived From Geological Provinces - Full Extent

• Derived From GLO Preliminary Assessment Extent

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

Under many aqueous conditions, metal oxide nanoparticles attract other nanoparticles and grow into fractal aggregates as the result of a balance between electrostatic and Van Der Waals interactions. Although particle coagulation has been studied for over a century, the effect of light on the state of aggregation is not well understood. Since nanoparticle mobility and toxicity have been shown to be a function of aggregate size, and generally increase as size decreases, photo-induced disaggregation may have significant effects. We show that ambient light and other light sources can partially disaggregate nanoparticles from the aggregates and increase the dermal transport of nanoparticles, such that small nanoparticle clusters can readily diffuse into and through the dermal profile, likely via the interstitial spaces. The discovery of photoinduced disaggregation presents a new phenomenon that has not been previously reported or considered in coagulation theory or transdermal toxicological paradigms. Our results show that after just a few minutes of light, the hydrodynamic diameter of TiO2 aggregates is reduced from ∼280 nm to ∼230 nm. We exposed pigskin to the nanoparticle suspension and found 200 mg kg−1 of TiO2 for skin that was exposed to nanoparticles in the presence of natural sunlight and only 75 mg kg−1 for skin exposed to dark conditions, indicating the influence of light on NP penetration. These results suggest that photoinduced disaggregation may have important health implications.

Gold nanoparticles linked to linear carboxylated dextran chains were attached to 3-aminopropyltriethoxysilane-functionalized glass surfaces. This method provides novel hybrid nanostructures on a surface with the unique optical properties of gold nanoparticles. The particles attached to the surface retain the capability to aggregate and disaggregate in response to their environment. This procedure presents an alternative method to the immobilization of gold nanoparticles onto planar substrates. Compared to gold nanoparticle monolayers, larger particle surface densities were obtained. Exposure to hydrophobic environments changes the conformation of the hydrophilic dextran chains, causing the gold nanoparticles to aggregate and inducing changes in the absorption spectrum such as red-shifting and broadening of the plasmon absorption peaks. These changes, characteristic of particle aggregation, are reversible. When the substrates are dried and then immersed in an aqueous environment, these changes can be visually observed in a reversible fashion and the sample changes color from the red color of colloidal gold to a bluish-purple color of aggregated nanoparticles. Surface-bound nanoparticles that retain their mobility when attached to a surface by means of a flexible polymer chain could expand the use of aggregation-based assays to solid substrates.